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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Fri, 23 Feb 2024 01:00:47 GMT2024-02-23T01:00:47ZLaueNN: neural-network-based hkl recognition of Laue spots and its application to polycrystalline materials
http://hdl.handle.net/10985/22223
LaueNN: neural-network-based hkl recognition of Laue spots and its application to polycrystalline materials
PURUSHOTTAM RAJ PUROHIT, Ravi Raj Purohit; TARDIF, Samuel; CASTELNAU, Olivier; EYMERY, Joel; GUINEBRETIÈRE, René; ROBACH, Odile; ORS, Taylan; MICHA, Jean-Sébastien
A feed-forward neural-network-based model is presented to index, in real time, the diffraction spots recorded during synchrotron X-ray Laue microdiffraction experiments. Data dimensionality reduction is applied to extract physical 1D features from the 2D X-ray diffraction Laue images, thereby making it possible to train a neural network on the fly for any crystal system. The capabilities of the LaueNN model are illustrated through three examples: a two-phase nanostructure, a textured high-symmetry specimen deformed in situ and a polycrystalline low-symmetry material. This work provides a novel way to efficiently index Laue spots in simple and complex recorded images in <1 s, thereby opening up avenues for the realization of real-time analysis of synchrotron Laue diffraction data.
Mon, 01 Aug 2022 00:00:00 GMThttp://hdl.handle.net/10985/222232022-08-01T00:00:00ZPURUSHOTTAM RAJ PUROHIT, Ravi Raj PurohitTARDIF, SamuelCASTELNAU, OlivierEYMERY, JoelGUINEBRETIÈRE, RenéROBACH, OdileORS, TaylanMICHA, Jean-SébastienA feed-forward neural-network-based model is presented to index, in real time, the diffraction spots recorded during synchrotron X-ray Laue microdiffraction experiments. Data dimensionality reduction is applied to extract physical 1D features from the 2D X-ray diffraction Laue images, thereby making it possible to train a neural network on the fly for any crystal system. The capabilities of the LaueNN model are illustrated through three examples: a two-phase nanostructure, a textured high-symmetry specimen deformed in situ and a polycrystalline low-symmetry material. This work provides a novel way to efficiently index Laue spots in simple and complex recorded images in <1 s, thereby opening up avenues for the realization of real-time analysis of synchrotron Laue diffraction data.LaueNN: neural-network-based hkl recognition of Laue spots and its application to polycrystalline materials
http://hdl.handle.net/10985/22708
LaueNN: neural-network-based hkl recognition of Laue spots and its application to polycrystalline materials
PURUSHOTTAM RAJ PUROHIT, Ravi Raj Purohit; TARDIF, Samuel; CASTELNAU, Olivier; EYMERY, Joel; GUINEBRETIÈRE, René; ROBACH, Odile; ORS, Taylan; MICHA, Jean-Sébastien
A feed-forward neural-network-based model is presented to index, in real time, the diffraction spots recorded during synchrotron X-ray Laue microdiffraction experiments. Data dimensionality reduction is applied to extract physical 1D features from the 2D X-ray diffraction Laue images, thereby making it possible to train a neural network on the ﬂy for any crystal system. The capabilities of the LaueNN model are illustrated through three examples: a two-phase nano-structure, a textured high-symmetry specimen deformed in situ and a polycrystalline low-symmetry material. This work provides a novel way to efﬁciently index Laue spots in simple and complex recorded images in <1 s, thereby opening up avenues for the realization of real-time analysis of synchrotron Laue diffraction data.
Wed, 01 Jun 2022 00:00:00 GMThttp://hdl.handle.net/10985/227082022-06-01T00:00:00ZPURUSHOTTAM RAJ PUROHIT, Ravi Raj PurohitTARDIF, SamuelCASTELNAU, OlivierEYMERY, JoelGUINEBRETIÈRE, RenéROBACH, OdileORS, TaylanMICHA, Jean-SébastienA feed-forward neural-network-based model is presented to index, in real time, the diffraction spots recorded during synchrotron X-ray Laue microdiffraction experiments. Data dimensionality reduction is applied to extract physical 1D features from the 2D X-ray diffraction Laue images, thereby making it possible to train a neural network on the ﬂy for any crystal system. The capabilities of the LaueNN model are illustrated through three examples: a two-phase nano-structure, a textured high-symmetry specimen deformed in situ and a polycrystalline low-symmetry material. This work provides a novel way to efﬁciently index Laue spots in simple and complex recorded images in <1 s, thereby opening up avenues for the realization of real-time analysis of synchrotron Laue diffraction data.Phase transition and twinning in polycrystals probed by in situ high temperature 3D reciprocal space mapping
http://hdl.handle.net/10985/23053
Phase transition and twinning in polycrystals probed by in situ high temperature 3D reciprocal space mapping
PURUSHOTTAM RAJ PUROHIT, Ravi Raj Purohit; FOWAN, Daniel Pepin; THUNE, Elsa; ARNAUD, Stephan; CHAHINE, Gilbert; BLANC, Nils; GUINEBRETIÈRE, René; CASTELNAU, Olivier
Polycrystalline materials exhibit physical properties that are driven by both the interatomic crystallographic structure as well as the nature and density of structural defects. Crystallographic evolutions driven by phase transitions and associated twinning process can be observed in situ in three-dimensional (3D) using monochromatic synchrotron radiation at very high temperatures (over 1000 C). This paper focuses on continuous measurements of the 3D-reciprocal space maps by high-resolution x-ray diffraction as a function of temperature along a phase transition process occurring between 1200 C and room temperature. These high precision measurements allow observing the reciprocal space node splitting and the evolution of the diffuse scattering signal around that node as a function of temperature. The capability of this experimental method is illustrated by direct in situ high temperature measurements of the 3D splitting of a reciprocal space node due to phase transition recorded on dense pure zirconia polycrystals.
Sat, 01 Oct 2022 00:00:00 GMThttp://hdl.handle.net/10985/230532022-10-01T00:00:00ZPURUSHOTTAM RAJ PUROHIT, Ravi Raj PurohitFOWAN, Daniel PepinTHUNE, ElsaARNAUD, StephanCHAHINE, GilbertBLANC, NilsGUINEBRETIÈRE, RenéCASTELNAU, OlivierPolycrystalline materials exhibit physical properties that are driven by both the interatomic crystallographic structure as well as the nature and density of structural defects. Crystallographic evolutions driven by phase transitions and associated twinning process can be observed in situ in three-dimensional (3D) using monochromatic synchrotron radiation at very high temperatures (over 1000 C). This paper focuses on continuous measurements of the 3D-reciprocal space maps by high-resolution x-ray diffraction as a function of temperature along a phase transition process occurring between 1200 C and room temperature. These high precision measurements allow observing the reciprocal space node splitting and the evolution of the diffuse scattering signal around that node as a function of temperature. The capability of this experimental method is illustrated by direct in situ high temperature measurements of the 3D splitting of a reciprocal space node due to phase transition recorded on dense pure zirconia polycrystals.Estimating single-crystal elastic constants of polycrystalline β metastable titanium alloy: A Bayesian inference analysis based on high energy X-ray diffraction and micromechanical modeling
http://hdl.handle.net/10985/19957
Estimating single-crystal elastic constants of polycrystalline β metastable titanium alloy: A Bayesian inference analysis based on high energy X-ray diffraction and micromechanical modeling
PURUSHOTTAM RAJ PUROHIT, Ravi Raj Purohit; RICHETON, Thiebaud; BERBENNI, Stephane; GERMAIN, Lionel; GEY, Nathalie; CONNOLLEY, Thomas; CASTELNAU, Olivier
A two-phase near- beta titanium alloy (Ti–10V–2Fe–3Al, or Ti-1023) in its as-forged state is employed to illustrate the feasibility of a Bayesian framework to identify single-crystal elastic constants (SEC). High Energy X-ray diffraction (HE-XRD) obtained at the Diamond synchrotron source are used to character- ize the evolution of lattice strains for various grain orientations during in situ specimen loading in the elastic regime. On the other hand, specimen behavior and grain deformation are estimated using the elastic self-consistent (ELSC) homogenization scheme. The XRD data and micromechanical modelling are revisited with a Bayesian framework. The effect of different material parameters (crystallographic and morphological textures, phase volume fraction) of the micromechanical model and the biases intro- duced by the XRD data on the identification of the SEC of the βphase are systematically investigated. In this respect, all the three cubic elastic constants of the βphase ( C11(beta) , C12(beta) , C44(beta) ) in the Ti-1023 alloy have been derived with their uncertainties. The grain aspect ratio in the ELSC model, which is often not considered in the literature, is found to be an important parameter in affecting the identified SEC. The Bayesian inference suggests a high probability for non-spherical grains (aspect ratio of ∼3 . 8+/-0 . 8 ) : C11(beta) = 92 . 6+/-19 . 1 GPa , C12(beta) = 82 . 5+/-16 . 3 GPa , C44(beta) = 43 . 5+/-7 . 1 GPa . The uncertainty obtained by Bayesian approach lies in the range of ~1-3 GPa for the shear modulus mu’ = (C11(beta) −C12(beta) )/2 , and ~7 GPa for the shear modulus mu’’ = C44(beta) , while it is significantly larger in the case of the bulk modulus (C11(beta) +2C12(beta))/3 (~17-24 GPa).
The authors also thank the Laboratoire Léon Brillouin (France) for beamtime allocation and Sebastien GAUTROT (LLB, France) for his help during the experiments. Vincent Jacquemain, Dr. Jean-Baptiste Marijon, and Dr. Stefan Michalik are acknowledged for their help during the synchrotron campaign at Diamond.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10985/199572021-01-01T00:00:00ZPURUSHOTTAM RAJ PUROHIT, Ravi Raj PurohitRICHETON, ThiebaudBERBENNI, StephaneGERMAIN, LionelGEY, NathalieCONNOLLEY, ThomasCASTELNAU, OlivierA two-phase near- beta titanium alloy (Ti–10V–2Fe–3Al, or Ti-1023) in its as-forged state is employed to illustrate the feasibility of a Bayesian framework to identify single-crystal elastic constants (SEC). High Energy X-ray diffraction (HE-XRD) obtained at the Diamond synchrotron source are used to character- ize the evolution of lattice strains for various grain orientations during in situ specimen loading in the elastic regime. On the other hand, specimen behavior and grain deformation are estimated using the elastic self-consistent (ELSC) homogenization scheme. The XRD data and micromechanical modelling are revisited with a Bayesian framework. The effect of different material parameters (crystallographic and morphological textures, phase volume fraction) of the micromechanical model and the biases intro- duced by the XRD data on the identification of the SEC of the βphase are systematically investigated. In this respect, all the three cubic elastic constants of the βphase ( C11(beta) , C12(beta) , C44(beta) ) in the Ti-1023 alloy have been derived with their uncertainties. The grain aspect ratio in the ELSC model, which is often not considered in the literature, is found to be an important parameter in affecting the identified SEC. The Bayesian inference suggests a high probability for non-spherical grains (aspect ratio of ∼3 . 8+/-0 . 8 ) : C11(beta) = 92 . 6+/-19 . 1 GPa , C12(beta) = 82 . 5+/-16 . 3 GPa , C44(beta) = 43 . 5+/-7 . 1 GPa . The uncertainty obtained by Bayesian approach lies in the range of ~1-3 GPa for the shear modulus mu’ = (C11(beta) −C12(beta) )/2 , and ~7 GPa for the shear modulus mu’’ = C44(beta) , while it is significantly larger in the case of the bulk modulus (C11(beta) +2C12(beta))/3 (~17-24 GPa).